Governed RAG systems with evals, red-teaming, and content filters.
Retrieval Augmented Generation (RAG)
- The Challenge Large Language Models (LLMs) are powerful but lack knowledge of your private, up-to-date information. This leads them to 'hallucinate' or make up answers, making them untrustworthy for business-critical tasks.
- Our Approach We build governed RAG systems that connect your LLM to your own authoritative knowledge bases. This grounds the model in facts, drastically reducing incorrect answers. We ensure reliability through rigorous evaluations, red-teaming, and content filters.
- Our Experience To help staff navigate complex rules, we built a RAG system that connected an LLM to internal policy manuals. This allowed employees to ask questions in natural language and receive accurate, verifiable answers instantly.
- The Outcomes Deploy LLM applications that are accurate, trustworthy, and grounded in your own data. Reduce the risk of misinformation and build user confidence with reliable, context-aware AI.
